Clarissa Wern Ting Wong is one of our 2020 winners for the HIEEC.
The economist Keynes once posited that when the quantitative calculation of expected utility can hardly help us make a decision, it is our “animal spirits” – or emotional states – that kick in and urge us into action (Keynes, 1936). This surmise proved prescient. From unbridled optimism that fuels an economic bubble, to stubbornness that undermines policies meant to raise social welfare, many suboptimal decision patterns that repeat themselves throughout history can be attributed to people’s “irrational” sides getting the better of them (Akerlof et al., 2009).
How We Make Suboptimal Decisions
A rational economic agent acting in accordance with expected utility theory is expected to evaluate a set of perfect information using mathematical logic: He calculates the utility he could gain from each option. Then, he chooses the option that maximises his utility within his budget constraints. However, real-life decisions are not informed purely by such mathematical calculations, but also by heuristics, biases and misconceptions which make us imperfect decision-makers.
Heuristics are mental shortcuts that people use to save time and mental effort in decision-making. Using these heuristics sometimes leads to statistically systematic errors in one’s information processing, also known as cognitive biases. For example, people tend to form an impression based on only a few salient examples that come to mind. This leads to the availability bias, whereby a minority of salient information disproportionately influences one’s judgement on the probability of something happening (Thaler, 2009). Resultant distortions in people’s probability judgements can lead them to make suboptimal decisions. For example, influenced by the preceding bull run in Internet stocks and the palpable optimism of fellow investors, investors in the 1990s came to display “irrational exuberance” in their expectations (Greenspan, 1996) and grossly overvalued Internet stocks. Energy consumers, on the other hand, tend to overconsume energy when they are not provided salient information like their level of energy usage (Thaler, 2009). People make suboptimal decisions when they over- or under-estimate an action’s utility: In one instance, important but non-salient information, like energy usage, is ignored; In another instance, salient but unrepresentative information, like overoptimistic expectations, is used to form the big picture.
Other cognitive biases can similarly distort one’s ability to make rational decisions. People tend to feel losses (shown as the red arrow in Figure 1) about twice as hard as gains (shown as the blue arrow) for the same change in wealth from a reference point (Tversky et. al, 2000). This leads them to exhibit loss aversion bias.
Loss aversion bias has been used to explain the endowment effect, where people tend to value goods they own higher than an identical good they do not own (Thaler, 2015). This is because they likely overvalue their loss in utility should they give up what they own. Consequences can be serious: If policymakers exhibit sufficient loss aversion, they may overprotect loss-making sectors, or develop anti-trade biases (Tovar, 2009).
Besides cognitive biases, misconceptions can also influence suboptimal decision making. As shown in Figure 2 below, World Bank development staff were generally shown to have believed that the poor were more suspicious of vaccines that they actually were. If such biases form assumptions in models which are used to predict an audience’s vaccine receptivity, this could lead to suboptimal allocation of resources for health outreach initiatives.
Separately, public health beneficiaries in developing countries can also hold misconceptions that impede policymakers’ efforts. In a South Asian nation, 35-50% of poor, lesser-educated women wrongly perceived the appropriate treatment for diarrhoea to be a reduction of water intake (World Bank, 2015). In fact, rehydration of the body is essential to treat diarrhoea. This caused the beneficiaries to undervalue the utility of the Oral Rehydration Therapy (ORT) programme, and thus under-consume it. Committing to misconceptions that are prevalent in a certain society can cause people to make suboptimal judgements.
Hitherto, we have discussed how biases, heuristics and misconceptions affect both governor and governed, both buyer and seller. If these factors result in suboptimal decision making, one forgoes the opportunity to choose an alternative option that would have yielded greater utility in the long run – avoiding over-buying inflated stocks, saving energy, bettering trade policies or receiving healthcare treatment.
The prevalence of sub-optimal decision-making implies that the application of traditional economic theory is limited in the real world. General Laws like the Law of Supply and Demand logically optimise resource allocation, but only if one makes rational choices. Influenced by a multitude of biases and heuristics and egged on by the lightning-speed pace of information spread, people inevitably make irrational choices. In the past, news of the Titanic’s sinking took hours to reach news outlets. Today, online tweets and posts make information, both real and fake, available instantaneously. Traders across the globe may be triggered to make knee-jerk, heuristic-influenced trading decisions. This increases the chance that market prices may overshoot their true value.
The prevalence of sub-optimal decision-making also sparks a re-examination of the soundness of traditional economic theory. Strict mathematical logic forms the bedrock of traditional economic tools’ axioms of rationality. It enables human behaviour to be modelled, albeit over-simplified. Expected Utility Functions, for example, model choice under risk, but the choices they model must, among other criteria, be well-defined and transitive (meaning if one prefers A to B and B to C, one prefers A to C) (Starmer, 2000). However, modern-day experiments show people defy these axioms: their preferences reverse when choices are differently framed, (Lichtenstein et. al, 1971) and as low as 8% of consumers’ choices follow transitive logic (Guadalupe et. al, 2020).
Behavioural economics research seemingly undermines the elegance and universality of application that traditional economics ventured to provide. However, it also presents opportunities for markets and policymakers to incorporate realities and mitigate inefficiencies.
Mitigating Inefficiencies
Expectation management
Expectations significantly drive buying and selling in the capital markets, just like how expectations of future food supply, rather than utility, have been shown to influence feeding behaviour in the animal kingdom (McNamara et. al, 2014). In addition to monetary policies such as the tweaking of interest rates, central banks including the US Federal Reserve engage in expectation management to smoothen out market recalibrations, thus reducing market volatility. When the Fed deems market expectations to have overvalued or undervalued a good, they intervene by releasing public statements hinting at interest rate changes. This cost-effective method has successfully readjusted market prices closer to their true value in numerous occasions (El-Erian, 2016).
Bias-checking mechanisms
Instituting bias-checking mechanisms can help policymakers weed out biases in the policy process. For example, the World Bank considered institutionalising “Red Teaming”, whereby an outsider group challenges a policymaking team’s assumptions, forcing policymakers to defend their views and become less prone to fall back on their prior biases (World Bank, 2015). 68% of IT companies that implemented a “Red Teaming” approach to tests of their IT systems achieved more effective tests (Exabeam, 2019).
Behavioural Insights
Behavioural insights, as shown below, can be used to optimise monetary policies such as basic needs subsidies. By leveraging on the powerful emotional attraction that a free product elicits, as well as the social norm it conveys that everyone should be using it, subsidising a good to zero cost, instead of low cost, can more than proportionately incentivise the take-up rate of a merit good, as shown in Figure 4 below. The subsidy dollar is thus maximised.
Behavioural insights on cognitive biases, like the Framing Effect, can help decision makers optimise consumer behaviour towards savings policies. Participation rates in US defined contribution savings plan jumped from 65% under an opt-in approach to a more ideal 98% under an automatic enrolment approach (Madrian et. al, 2001). The automatic enrolment approach mitigated workers’ “irrational” inertia in signing up for plans, strategically framing consumer decisions for optimal socio-economic outcomes.
To apply behavioural economics’ insights, policy approach must shift from the process of pure traditional modelling and then applying models: case-specific empirical research must first be done to identify “irrational” quirks undergirding societal behaviour. Additionally, plans must likely be reviewed, with a rigorous feedback loop in place, to evolve with changing consumer behaviour. In Figure 4, multiple data points had to be collected before policymakers could identify a behavioural trend.
Our “animal spirits” may lead us astray from rational choice. Yet, once we recognise them and harness them positively, the very same “animal spirits” can lead our herd to greener pastures, collectively.
References
Akerlof, G. A., & Shiller, R. J. (2009). Animal spirits: How human psychology drives the economy, and why it matters for global capitalism. Princeton, NJ: Princeton University Press.
El-Erian, M. (2016, May 23). The Fed, the markets and expectation management. The Financial Times. Retrieved 2020, from https://www.ft.com/content/fb033693-82ed-30bd a4ee-0f5851025440
Exabeam. (2019, August 15). More than One-third of Security Professionals’ Defensive Blue Teams Fail to Catch Offensive Red Teams, According to Exabeam Study [Press release]. Retrieved from https://www.exabeam.com/pr/more-than-one-third-of-security professionals-defensive-blue-teams-fail-to-catch-offensive-red-teams-according-to exabeam-study/
Greenspan, A. (1996). Remarks by Chairman Alan Greenspan At the Annual Dinner and Francis Boyer Lecture of The American Enterprise Institute for Public Policy Research, Washington, D.C. [Transcript]. Retrieved from https://www.federalreserve.gov/boarddocs/speeches/1996/19961205.htm
Guadalupe-Lanas, J., Cruz-Cárdenas, J., Artola-Jarrín, V., & Palacio-Fierro, A. (2020). Empirical evidence for intransitivity in consumer preferences. Heliyon, 6(3). doi:10.1016/j.heliyon.2020.e03459
Keynes, John M. (1936). The General Theory of Employment, Interest and Money. London. Macmillan. pp. 161-162.
Lichtenstein, S., & Slovic, P. (1971). Reversals of preference between bids and choices in gambling decisions. Journal of Experimental Psychology, 89(1), 46-55. doi:10.1037/h0031207
Madrian, B. C., & Shea, D. F. (2001). The Power of Suggestion: Inertia in 401(k) Participation and Savings Behavior. The Quarterly Journal of Economics, 116(4), 1149-1225. doi:10.1162/003355301753265543
McNamara, J. M., Trimmer, P. C., & Houston, A. I. (2014). Natural selection can favour ‘irrational’ behaviour. Biology Letters, 10(1), 20130935. doi:10.1098/rsbl.2013.0935
Starmer, C. (2000). Developments in Non-Expected Utility Theory: The Hunt for a Descriptive Theory of Choice under Risk. Journal of Economic Literature, 38(2), 332-382. doi:10.1257/jel.38.2.332
Thaler, R. H., & Sunstein, C. R. (2009). Nudge: Improving decisions about health, wealth, and happiness: Rev. and exp. ed. New York: Penguin. pp. 24-26, 196
Thaler, R. H. (2015). Misbehaving: The Making of Behavioral Economics. New York, NY: W. W. Norton & Company. pp. 12-19
Tovar, P. (2009). The effects of loss aversion on trade policy: Theory and evidence. Journal of International Economics, 78(1), 154-167. doi:10.1016/j.jinteco.2009.01.012
Tversky, A., & Kahneman, D. (2000). Advances in Prospect Theory: Cumulative Representation of Uncertainty. Choices, Values, and Frames, 44-66. doi:10.1017/cbo9780511803475.004
World Bank. (2015). World Development Report 2015: Mind, Society, and Behavior. Washington, DC: World Bank. doi: 10.1596/978-1-4648-0342-0.
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